Manage LLM context window overflow

Optimize LLM context windows via summarization/trimming/routing

Multi-turn conversations where context is overflowing, improving model behavior when fed too much historical data.

• · Conversation history (messages array)
• · Token budget (max context size)

• · Optimized message list
• · Summary of pruned content
• · Token budget allocation plan

• · tiktoken (OpenAI tokenizer)
• · LangChain
• · Claude API (200K+ context)

Knowledge of tokenization basics, target model context limits

Over-aggressive summarization loses critical information, context rot from repeated truncation, token counting inaccuracy

• · Tiered context strategy (full → summarize → RAG)
• · Serial position optimization (primacy/recency weighting)
• · Token budget allocation patterns